scholarly journals Highway Traffic State Estimation and Short-term Prediction

2016 ◽  
Author(s):  
Andreas Allström ◽  
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Short Term ◽  
Traffic State ◽  
Term Prediction ◽  
Traffic State Estimation ◽  
Short Term Prediction

scholarly journals Highway Traffic State Estimation With Mixed Connected and Conventional Vehicles

2016 ◽  
Vol 17 (12) ◽  
pp. 3484-3497 ◽  
Author(s):  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Traffic State ◽  
Traffic State Estimation

scholarly journals On the Short-term Prediction of Traffic State: An Application on Urban Freeways in ROME

2015 ◽  
Vol 10 ◽  
pp. 176-185 ◽  
Author(s):  
L. Mannini ◽  
S. Carrese ◽  
E. Cipriani ◽  
U. Crisalli
Keyword(s):  
Short Term ◽  
Traffic State ◽  
Term Prediction ◽  
Short Term Prediction

scholarly journals Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Arash Rasaizadi ◽  
Seyedehsan Seyedabrishami ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Road Traffic ◽  
Support Vector ◽  
Paper Machine ◽  
Short Term ◽  
Traffic State ◽  
Rural Road ◽  
Term Prediction ◽  
Short Term Prediction

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

scholarly journals Highway traffic state estimation with mixed connected and conventional vehicles: Microscopic simulation-based testing

2017 ◽  
Vol 78 ◽  
pp. 13-33 ◽  
Author(s):  
Markos Fountoulakis ◽  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Ioannis Papamichail ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Microscopic Simulation ◽  
Traffic State ◽  
Traffic State Estimation ◽  
Simulation Based

scholarly journals Highway Traffic State Estimation with Mixed Connected and Conventional Vehicles

2016 ◽  
Vol 49 (3) ◽  
pp. 309-314
Author(s):  
Nikolaos Bekiaris-Liberis ◽  
Claudio Roncoli ◽  
Markos Papageorgiou
Keyword(s):  
State Estimation ◽  
Highway Traffic ◽  
Traffic State ◽  
Traffic State Estimation
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